Analysis CATegories
Anonymized
26 September, 2023
Load packages
library(tidyverse)
library(janitor)
library(cowplot)
library(here)
library(readxl)
library(Matrix)
library(lme4)
library(lmerTest)
library(TOSTER)
library(eyetrackingR)
theme_set(theme_cowplot())
knitr::opts_chunk$set(cache = FALSE, warn = FALSE,warning=FALSE, message = FALSE)Load data
data_file_path <- here::here("data_analysis","registered_report","data","processed_data","CATegories_exp2_processed_data_anonymized.csv")
d <- read_csv(data_file_path)
## add general category properties from typicality dataset
## typicality
animal_rating_names <- read.csv(here::here("data_analysis","registered_report","data","processed_data","animal_ratings_stimuli_full.csv"))
animal_stims <- unique(c(unique(d$left_image),unique(d$right_image)))
typicality <- read.csv(here::here("data_analysis","registered_report","data","processed_data","typicality_animals_summarized.csv")) %>%
mutate(item_name=str_remove(animal_name,pattern=" ")) %>%
left_join(animal_rating_names) %>%
mutate(image_name_resized = str_replace(image_experiment_name,".jpg","")) %>%
filter(image_name_resized %in% animal_stims)
d <- d %>%
left_join(typicality %>% select(image_name_resized,category,typicality_subjective),by=c("target_image" = "image_name_resized")) %>%
rename(typicality_condition=typicality_subjective)%>%
filter(400<age & age<600) #filter out children outside of the age boundsSummarize participant information
#summarize subj info
subj_info_multisession <- d %>%
distinct(sub_num, age,months,age_mo,child_gender,trial_order) %>%
mutate(
age_mo_c = age_mo - mean(age_mo),
age_c = age - mean(age)
)
subj_info <- d %>%
distinct(sub_num,child_gender) %>%
summarize(
N = n(),
N_female = sum(child_gender=="f")
)
overall_subj_info <- subj_info_multisession %>%
summarize(
N = length(unique(sub_num)),
sessions = n(),
mean_age = mean(age_mo),
min_age = min(age),
max_age = max(age),
sd_age = sd(age_mo)
) %>%
left_join(subj_info)
overall_subj_info %>%
knitr::kable()| N | sessions | mean_age | min_age | max_age | sd_age | N_female |
|---|---|---|---|---|---|---|
| 133 | 235 | 15.70553 | 410 | 584 | 1.554924 | 67 |
Useable trial summary
In order for a trial to be included, participants must contribute at least 50% looking during the windows of interest when computing baseline-corrected proportion target looking: the critical window (300 ms - 2800 ms relative to target word onset) and the baseline window (-2000 ms - 0 ms relative to target word onset).
critical_window <- c(300,2800)
baseline_window <- c(-2000,0)
summarize_subj_useable_trials_critical_window <- d %>%
filter(corrected_time_centered>=critical_window[1]&corrected_time_centered<=critical_window[2]) %>%
group_by(sub_num,age,age_mo, child_gender, session,trial_order,trial_number,target_image,target_typicality_z,condition) %>%
summarize(
length_critical_window=n(),
useable_frames_critical_window=sum(!is.na(accuracy_transformed)),
percent_useable_critical_window=useable_frames_critical_window/length_critical_window,
useable_critical_window=ifelse(percent_useable_critical_window>=0.5,1,0), #useable if at least 50% looking
mean_target_looking_critical=mean(accuracy_transformed,na.rm=TRUE)
)
summarize_subj_useable_trials_baseline_window <- d %>%
filter(corrected_time_centered>=baseline_window[1] & corrected_time_centered<=baseline_window[2]) %>%
group_by(sub_num, session,age,age_mo, child_gender, trial_order,trial_number,target_image,target_typicality_z,condition) %>%
summarize(
length_baseline_window=n(),
useable_frames_baseline_window=sum(!is.na(accuracy_transformed)),
percent_useable_baseline_window=useable_frames_baseline_window/length_baseline_window,
useable_baseline_window=ifelse(percent_useable_baseline_window>=0.5,1,0), #useable if at least 50% looking
mean_target_looking_baseline=mean(accuracy_transformed,na.rm=TRUE)
)
#overall useable trials
summarize_subj_useable_trials <- summarize_subj_useable_trials_critical_window %>%
left_join(summarize_subj_useable_trials_baseline_window) %>%
mutate(
useable_window = ifelse(useable_baseline_window==1&useable_critical_window==1,1,0),
corrected_target_looking = mean_target_looking_critical - mean_target_looking_baseline
)
summarize_useable_trials <- summarize_subj_useable_trials %>%
group_by(sub_num, age, child_gender, session,trial_order) %>%
summarize(
num_useable_trials=sum(useable_window),
num_useable_trials_critical_window = sum(useable_critical_window)
)
#total trials
summarize_subj_trials <- summarize_useable_trials %>%
ungroup() %>%
group_by(sub_num) %>%
summarize(
session_num = n(),
total_trials = sum(num_useable_trials),
total_trials_critical_window = sum(num_useable_trials_critical_window),
exclude_participant = ifelse(total_trials<24,1,0),
exclude_participant_critical = ifelse(total_trials_critical_window<24,1,0)
)
#average trials contributed
mean(summarize_subj_trials$total_trials)## [1] 29.25564#participants to exclude based on data contribution
sum(summarize_subj_trials$exclude_participant)## [1] 47#join with main data frame
summarize_useable_trials <- summarize_useable_trials %>%
left_join(summarize_subj_trials)
d <- d %>%
left_join(summarize_useable_trials) %>%
left_join(summarize_subj_useable_trials)
summarize_useable_trials_wide <- summarize_useable_trials %>%
ungroup() %>%
select(sub_num,session_num,total_trials,exclude_participant,session,num_useable_trials) %>%
group_by(sub_num,session_num,total_trials,exclude_participant) %>%
pivot_wider(
names_from = "session",
names_prefix = "num_trials_session_",
values_from = "num_useable_trials"
)
#write out useable trial summary
write_csv(summarize_useable_trials_wide,here::here("data_analysis","registered_report","data","processed_data","CATegories_exp2_useable_trial_summary.csv"))Overall, among the trials contributed by the 133 participants, 82.5% of trials contained sufficient looking to meet our trial-level inclusion criteria (at least 50% looking during both the baseline window and the critical window). 86 of the 133 participants contributed sufficient looking data on at least half of the experimental trials (overall M = 29.3)
Summarize subject-level accuracy
Overall
Here, we summarize each participants’ average accuracy during the critical window and average baseline-corrected proportion target looking.
# critical window only
## trial-level
trial_critical_window_accuracy <- d %>%
filter(exclude_participant_critical==0) %>%
filter(useable_critical_window==1) %>%
filter(corrected_time_centered>=300&corrected_time_centered<=2800) %>%
group_by(sub_num, age,age_mo, child_gender, trial_order,trial_number,category,target_image,target_typicality_z,condition) %>%
summarize(mean_accuracy=mean(accuracy_transformed,na.rm=TRUE))
## average
avg_critical_window_accuracy <- trial_critical_window_accuracy %>%
ungroup() %>%
group_by(sub_num, child_gender) %>%
summarize(N=n(),
mean_age = mean(age),
mean_age_mo = mean(age_mo),
accuracy=mean(mean_accuracy,na.rm=TRUE),
ci=qt(0.975, N-1)*sd(mean_accuracy,na.rm=T)/sqrt(N),
lower_ci=accuracy-ci,
upper_ci=accuracy+ci)
#baseline-corrected target looking
## trial-level
trial_corrected_accuracy <- d %>%
filter(exclude_participant==0) %>%
filter(useable_window==1) %>%
distinct(sub_num,session, age,age_mo, child_gender, trial_order,trial_number,category,target_image,target_typicality_z, condition,mean_target_looking_critical,mean_target_looking_baseline,corrected_target_looking)
## average
avg_corrected_target_looking <- trial_corrected_accuracy %>%
group_by(sub_num, child_gender) %>%
summarize(N=n(),
average_corrected_target_looking=mean(corrected_target_looking,na.rm=TRUE),
ci=qt(0.975, N-1)*sd(corrected_target_looking,na.rm=T)/sqrt(N),
lower_ci=average_corrected_target_looking-ci,
upper_ci=average_corrected_target_looking+ci)By Typicality Condition
Here, we summarize each participants’ average accuracy during the critical window and average baseline-corrected proportion target looking.
# critical window only
avg_critical_window_accuracy_by_typicality <- d %>%
filter(exclude_participant_critical==0) %>%
filter(useable_critical_window==1) %>%
filter(corrected_time_centered>=300&corrected_time_centered<=2800) %>%
group_by(sub_num, age,age_mo, child_gender, trial_order,trial_number,target_image,target_typicality_z,condition) %>%
summarize(mean_accuracy=mean(accuracy_transformed,na.rm=TRUE)) %>%
ungroup() %>%
group_by(sub_num, child_gender,condition) %>%
summarize(N=n(),
mean_age = mean(age),
mean_age_mo = mean(age_mo),
accuracy=mean(mean_accuracy,na.rm=TRUE),
ci=qt(0.975, N-1)*sd(mean_accuracy,na.rm=T)/sqrt(N),
lower_ci=accuracy-ci,
upper_ci=accuracy+ci)
#baseline-corrected target looking
avg_corrected_target_looking_by_typicality <- d %>%
filter(exclude_participant==0) %>%
filter(useable_window==1) %>%
distinct(sub_num, age,age_mo, child_gender, trial_order,trial_number,target_image,target_typicality_z,condition,mean_target_looking_critical,mean_target_looking_baseline,corrected_target_looking) %>%
group_by(sub_num, child_gender,condition) %>%
summarize(N=n(),
average_corrected_target_looking=mean(corrected_target_looking,na.rm=TRUE),
ci=qt(0.975, N-1)*sd(corrected_target_looking,na.rm=T)/sqrt(N),
lower_ci=average_corrected_target_looking-ci,
upper_ci=average_corrected_target_looking+ci)Models
Aim 1
1.1
avg_corrected_target_looking_by_typicality <- avg_corrected_target_looking_by_typicality %>%
mutate(
typicality_condition_c = case_when(
condition == "atypical" ~ -0.5,
condition == "typical" ~ 0.5,
TRUE ~ NA_real_
),
typicality_condition_typ = case_when(
condition == "atypical" ~ -1,
condition == "typical" ~ 0,
TRUE ~ NA_real_
),
typicality_condition_atyp = case_when(
condition == "atypical" ~ 0,
condition == "typical" ~ 1,
TRUE ~ NA_real_
),
)
m_1_1 <- lmer(average_corrected_target_looking ~ 1 + typicality_condition_c + (1|sub_num),data=avg_corrected_target_looking_by_typicality)
summary(m_1_1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: average_corrected_target_looking ~ 1 + typicality_condition_c +
## (1 | sub_num)
## Data: avg_corrected_target_looking_by_typicality
##
## REML criterion at convergence: -318.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.29665 -0.49235 0.08458 0.57549 2.54722
##
## Random effects:
## Groups Name Variance Std.Dev.
## sub_num (Intercept) 0.003003 0.05480
## Residual 0.006035 0.07769
## Number of obs: 172, groups: sub_num, 86
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.066998 0.008367 85.000000 8.007 5.47e-12 ***
## typicality_condition_c 0.021514 0.011847 85.000000 1.816 0.0729 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## typclty_cn_ 0.000confint(m_1_1,method="Wald")## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) 0.050598842 0.08339743
## typicality_condition_c -0.001706766 0.044733831.1.1.
Yes, infants significantly recognized the target word.
1.1.2
No significant effect of typicality
Equivalence test - can’t reject equivalence test
overall_condition_summary <- avg_corrected_target_looking_by_typicality %>%
group_by(sub_num) %>%
summarize(
condition_diff = average_corrected_target_looking[condition=="typical"]-average_corrected_target_looking[condition=="atypical"]
) %>%
ungroup() %>%
summarize(
N=n(),
diff = mean(condition_diff),
sd = sd(condition_diff)
)
tsum_TOST(m1=overall_condition_summary$diff,sd1=overall_condition_summary$sd,n1=overall_condition_summary$N,eqb=0.25, eqbound_type = "SMD")##
## One-sample t-Test
##
## The equivalence test was non-significant, t(85) = -0.503, p = 3.08e-01
## The null hypothesis test was non-significant, t(85) = 1.816, p = 7.29e-02
## NHST: don't reject null significance hypothesis that the effect is equal to zero
## TOST: don't reject null equivalence hypothesis
##
## TOST Results
## t df p.value
## t-test 1.8159 85 0.073
## TOST Lower 4.1343 85 < 0.001
## TOST Upper -0.5025 85 0.308
##
## Effect Sizes
## Estimate SE C.I. Conf. Level
## Raw 0.02151 0.01185 [0.0018, 0.0412] 0.9
## Hedges's g 0.19408 0.10884 [0.016, 0.371] 0.9
## Note: SMD confidence intervals are an approximation. See vignette("SMD_calcs").1.1.3
m_1_1_3_typ <- lmer(average_corrected_target_looking ~ 1 + typicality_condition_typ + (1|sub_num),data=avg_corrected_target_looking_by_typicality)
summary(m_1_1_3_typ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: average_corrected_target_looking ~ 1 + typicality_condition_typ +
## (1 | sub_num)
## Data: avg_corrected_target_looking_by_typicality
##
## REML criterion at convergence: -318.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.29665 -0.49235 0.08458 0.57549 2.54722
##
## Random effects:
## Groups Name Variance Std.Dev.
## sub_num (Intercept) 0.003003 0.05480
## Residual 0.006035 0.07769
## Number of obs: 172, groups: sub_num, 86
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.07775 0.01025 153.09909 7.585 3.01e-12 ***
## typicality_condition_typ 0.02151 0.01185 85.00000 1.816 0.0729 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## typclty_cn_ 0.578confint(m_1_1_3_typ,method="Wald")## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) 0.057661808 0.09784799
## typicality_condition_typ -0.001706766 0.04473383Infants successfully recognize words in the typical condition.
m_1_1_3_atyp <- lmer(average_corrected_target_looking ~ 1 + typicality_condition_atyp + (1|sub_num),data=avg_corrected_target_looking_by_typicality)
summary(m_1_1_3_atyp)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: average_corrected_target_looking ~ 1 + typicality_condition_atyp +
## (1 | sub_num)
## Data: avg_corrected_target_looking_by_typicality
##
## REML criterion at convergence: -318.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.29665 -0.49235 0.08458 0.57549 2.54722
##
## Random effects:
## Groups Name Variance Std.Dev.
## sub_num (Intercept) 0.003003 0.05480
## Residual 0.006035 0.07769
## Number of obs: 172, groups: sub_num, 86
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.05624 0.01025 153.09909 5.486 1.66e-07 ***
## typicality_condition_atyp 0.02151 0.01185 85.00000 1.816 0.0729 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## typclty_cn_ -0.578confint(m_1_1_3_atyp,method="Wald")## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) 0.036148277 0.07633446
## typicality_condition_atyp -0.001706766 0.04473383Infants successfully recognize words in the atypical condition.
1.2
trial_corrected_accuracy <- trial_corrected_accuracy %>%
mutate(
typicality_condition_c = case_when(
condition == "atypical" ~ -0.5,
condition == "typical" ~ 0.5,
TRUE ~ NA_real_
),
typicality_condition_typ = case_when(
condition == "atypical" ~ -1,
condition == "typical" ~ 0,
TRUE ~ NA_real_
),
typicality_condition_atyp = case_when(
condition == "atypical" ~ 0,
condition == "typical" ~ 1,
TRUE ~ NA_real_
),
)
m_1_2 <- lmer(corrected_target_looking ~ 1 + typicality_condition_c +
(1 + typicality_condition_c|sub_num) +
(1|category),
data=trial_corrected_accuracy)
summary(m_1_2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: corrected_target_looking ~ 1 + typicality_condition_c + (1 +
## typicality_condition_c | sub_num) + (1 | category)
## Data: trial_corrected_accuracy
##
## REML criterion at convergence: 2332.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.15368 -0.63205 -0.02159 0.67215 2.77470
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## sub_num (Intercept) 0.0028254 0.05315
## typicality_condition_c 0.0001944 0.01394 1.00
## category (Intercept) 0.0001320 0.01149
## Residual 0.1194800 0.34566
## Number of obs: 3173, groups: sub_num, 86; category, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.811e-02 1.021e-02 6.265e+00 6.673 0.000458 ***
## typicality_condition_c 1.772e-02 1.238e-02 1.078e+03 1.431 0.152634
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## typclty_cn_ 0.068
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')1.3
Resampling
In order to plot participants’ average proportion looking to the target across the trial, we smooth/ resample time. This is necessary when plotting the timecourses given the variable sampling rate in the data (otherwise the mean observations “jump around” due to varying contributing data composition at different time points).
target_ms_per_frame=1000/30
#adapted from: https://github.com/langcog/peekds/blob/master/R/generate_aoi.R
resample_trial <- function(df_trial) {
t_origin <- df_trial$corrected_time_centered
data_origin <- df_trial$accuracy_transformed
# create the new timestamps for resampling
t_start <- min(t_origin) - (min(t_origin) %% target_ms_per_frame)
t_resampled <- seq(from = t_start, to = max(t_origin),
by = target_ms_per_frame)
# exchange strings values with integers for resampling
# this step critical for interpolating missing vals quickly and correctly
aoi_num <- data_origin %>%
dplyr::recode(.missing = 2) #recode NA as 2
# start resampling with approx
aoi_resampled <- stats::approx(x = t_origin, y = aoi_num, xout = t_resampled,
method = "constant", rule = 2,
ties = "ordered")$y
aoi_resampled_recoded <- aoi_resampled %>%
dplyr::recode("0"="0","1"="1","2" = "missing") %>%
as.numeric()
# adding back the columns to match schema
dplyr::tibble(corrected_time_centered = t_resampled,
accuracy_transformed = aoi_resampled_recoded,
trial_number = df_trial$trial_number[1],
sub_num = df_trial$sub_num[1])
}
d_resampled <- d %>%
dplyr::mutate(sub_num_trial_number = paste(.data$sub_num,
.data$trial_number, sep = "_")) %>%
split(.$sub_num_trial_number) %>%
purrr::map_df(resample_trial) %>%
dplyr::arrange(.data$sub_num, .data$trial_number)
d_info <- d %>%
select(-corrected_time_centered,-accuracy_transformed) %>%
distinct(sub_num, exclude_participant, useable_window, age,age_mo, child_gender, trial_order, condition, trial_order,trial_number,target_image,target_typicality_z)
d_resampled <- d_resampled %>%
left_join(d_info) %>%
mutate(corrected_time_centered =round(corrected_time_centered,0))Timecourse analysis - cluster-based permutation analysis
Next, we prepare the data for use with the eyetrackingR package
d_eyetrackingr <- d_resampled %>%
filter(exclude_participant==0) %>%
filter(useable_window==1) %>%
unite("unique_trial",trial_order,trial_number,sep="_",remove=FALSE) %>%
mutate(
target = case_when(
is.na(accuracy_transformed) ~ NA,
accuracy_transformed == 1 ~ TRUE,
accuracy_transformed == 0 ~ FALSE,
),
distractor = case_when(
is.na(accuracy_transformed) ~ NA,
accuracy_transformed == 0 ~ TRUE,
accuracy_transformed == 1 ~ FALSE,
),
trackloss = case_when(
is.na(accuracy_transformed) ~ TRUE,
TRUE ~ FALSE
)
) %>%
make_eyetrackingr_data(
participant_column = "sub_num",
trial_column = "unique_trial",
time_column = "corrected_time_centered",
trackloss_column = "trackloss",
aoi_columns = c("target","distractor"),
treat_non_aoi_looks_as_missing = TRUE
)
response_window <- subset_by_window(
d_eyetrackingr,
window_start_time = 300,
window_end_time = 2800,
rezero=FALSE
)
summary_data_loss <- describe_data(response_window, 'target', 'sub_num')
response_time <- make_time_sequence_data(response_window,
time_bin_size = 100,
predictor_columns = c("condition"),
aois = "target",
summarize_by = "sub_num" )
# visualize timecourse
plot(response_time, predictor_column = "condition") +
theme_light() +
coord_cartesian(ylim = c(0,1))
#divergence analysis
# tb_analysis <- analyze_time_bins(data = response_time, predictor_column = "condition", test= 'boot_splines', within_subj = TRUE, bs_samples = 1000, alpha = .05/num_time_bins)
# plot(tb_analysis) + theme_light()
# summary(tb_analysis)
#bootstrapped cluster-based permutation analysis
n_samples <- 100
threshold_t <- 2
df_timeclust <- make_time_cluster_data(response_time,
test= "t.test", paired=TRUE,
predictor_column = "condition",
threshold = threshold_t)
plot(df_timeclust) + ylab("T-Statistic") + theme_light()
summary(df_timeclust)## Test Type: t.test
## Predictor: condition
## Formula: Prop ~ condition
## Summary of Clusters ======
## [1] Cluster Direction EndTime
## <0 rows> (or 0-length row.names)clust_analysis <- analyze_time_clusters(df_timeclust, within_subj=TRUE, paired=TRUE,
samples=n_samples)
plot(clust_analysis) + theme_light()
summary(clust_analysis)## Test Type: t.test
## Predictor: condition
## Formula: Prop ~ condition
## Null Distribution ======
## Mean: -0.1232
## 2.5%: -9.8299
## 97.5%: 10.6581
## Summary of Clusters ======
## [1] Cluster Direction EndTime Probability
## <0 rows> (or 0-length row.names)Timecourse Plot
Next, we plot the data. First we summarize the data in two steps: (1) summarize the data by subject for each time point, followed by (2) averaging looking for each time point across subjects.
#summarizing within subject for each time point
summarize_subj <- d_resampled %>%
filter(exclude_participant==0) %>%
filter(useable_window==1) %>%
group_by(sub_num, age,age_mo, child_gender, trial_order, corrected_time_centered) %>%
summarize(N=n(),
non_na_n = sum(!is.na(accuracy_transformed)),
mean_accuracy=mean(accuracy_transformed,na.rm=TRUE),
ci=qt(0.975, non_na_n-1)*sd(accuracy_transformed,na.rm=T)/sqrt(non_na_n),
lower_ci=mean_accuracy-ci,
upper_ci=mean_accuracy+ci) %>%
ungroup()
#summarizing across subjects for each time point
summarize_across_subj <- summarize_subj %>%
group_by(corrected_time_centered) %>%
dplyr::summarize(n=n(),
accuracy=mean(mean_accuracy,na.rm=TRUE),
sd_accuracy=sd(mean_accuracy,na.rm=TRUE),
se_accuracy=sd_accuracy/sqrt(n))
ggplot(summarize_across_subj,aes(corrected_time_centered,accuracy))+
xlim(-2000,4000)+
geom_smooth(method="gam")+
geom_errorbar(aes(ymin=accuracy-se_accuracy,ymax=accuracy+se_accuracy),width=0)+
geom_point()+
geom_vline(xintercept=0,size=1.5)+
geom_hline(yintercept=0.5,size=1.2,linetype="dashed")+
geom_vline(xintercept=300,linetype="dotted")+
ylim(0.35,0.65)
ggsave(here::here("figures","overall_accuracy.png"))Timecourse by age
summarize_across_subj_by_age <- summarize_subj %>%
mutate(age_group=ifelse(age_mo>16,"older than 16 months","younger than 16 months")) %>%
group_by(age_group,corrected_time_centered) %>%
dplyr::summarize(n=n(),
accuracy=mean(mean_accuracy,na.rm=TRUE),
sd_accuracy=sd(mean_accuracy,na.rm=TRUE),
se_accuracy=sd_accuracy/sqrt(n))
ggplot(summarize_across_subj_by_age,aes(corrected_time_centered,accuracy))+
xlim(-2000,4000)+
geom_smooth(method="gam")+
geom_errorbar(aes(ymin=accuracy-se_accuracy,ymax=accuracy+se_accuracy),width=0)+
geom_point()+
geom_vline(xintercept=0,size=1.5)+
geom_hline(yintercept=0.5,size=1.2,linetype="dashed")+
geom_vline(xintercept=300,linetype="dotted")+
facet_wrap(~age_group)
ggsave(here::here("figures","overall_accuracy_by_age.png"),width=12, height=9)Timecourse by condition
summarize_subj_condition <- d_resampled %>%
filter(exclude_participant==0) %>%
filter(useable_window==1) %>%
group_by(sub_num, age,age_mo, child_gender, condition, trial_order, corrected_time_centered) %>%
summarize(mean_accuracy=mean(accuracy_transformed,na.rm=TRUE))
summarize_across_subj_cond <- summarize_subj_condition %>%
group_by(condition,corrected_time_centered) %>%
summarize(n=n(),
accuracy=mean(mean_accuracy,na.rm=TRUE),
sd_accuracy=sd(mean_accuracy,na.rm=TRUE),
se_accuracy=sd_accuracy/sqrt(n))
num_subjects <- summarize_across_subj_cond %>%
group_by()%>%
summarize(max_subnum=max(n))
summarize_across_subj_cond<- summarize_subj_condition %>%
group_by(condition,corrected_time_centered) %>%
summarize(n=n(),
accuracy=mean(mean_accuracy,na.rm=TRUE),
sd_accuracy=sd(mean_accuracy,na.rm=TRUE),
se_accuracy=sd_accuracy/sqrt(n))
ggplot(summarize_across_subj_cond,aes(corrected_time_centered,accuracy,color=condition))+
xlim(-2500,4000)+
geom_rect(data = data.frame(xmin = 300,
xmax = 2800,
ymin = -Inf,
ymax = Inf),
aes(x=NULL, y=NULL,xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax,color=NULL),
fill = "grey", alpha = 0.2)+
geom_rect(data = data.frame(xmin = -2000,
xmax = 0,
ymin = -Inf,
ymax = Inf),
aes(x=NULL, y=NULL,xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax,color=NULL),
fill = "grey", alpha = 0.2)+
geom_errorbar(aes(ymin=accuracy-se_accuracy,ymax=accuracy+se_accuracy),width=0)+
geom_point(alpha=0.5)+
geom_smooth(data=summarize_subj_condition,aes(y=mean_accuracy),method="gam")+
geom_vline(xintercept=0,size=1.5)+
geom_hline(yintercept=0.5,size=1.2,linetype="dashed")+
geom_vline(xintercept=300,linetype="dotted")+
geom_vline(xintercept=2800,linetype="dotted")+
geom_vline(xintercept=-2000,linetype="dotted")+
geom_vline(xintercept=0,linetype="dotted")+
theme(legend.position = c(0.75,0.15))+
annotate("text",label="Critical Window",x=1550,y=0.9)+
annotate("text",label="Baseline Window",x=-1000,y=0.9)+
ylim(0,1)+
ylab("Proportion Target Looking")+
xlab("Time (centered on target word onset, in ms)")
ggsave(here::here("figures","typicality_accuracy.png"),width=10,height=6)Aim 2
Change across age
trial_corrected_accuracy <- trial_corrected_accuracy %>%
left_join(subj_info_multisession)
m_2 <- lmer(corrected_target_looking ~ 1 + typicality_condition_c * age_mo_c +
(1 + typicality_condition_c|sub_num) +
(1|category),
data=trial_corrected_accuracy)
summary(m_2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: corrected_target_looking ~ 1 + typicality_condition_c * age_mo_c +
## (1 + typicality_condition_c | sub_num) + (1 | category)
## Data: trial_corrected_accuracy
##
## REML criterion at convergence: 2343
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.11458 -0.64949 -0.01878 0.67391 2.80414
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## sub_num (Intercept) 0.0025090 0.05009
## typicality_condition_c 0.0001211 0.01101 1.00
## category (Intercept) 0.0001351 0.01163
## Residual 0.1195285 0.34573
## Number of obs: 3173, groups: sub_num, 86; category, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.683e-02 1.008e-02 5.859e+00 6.630 0.000626
## typicality_condition_c 1.688e-02 1.239e-02 1.380e+03 1.362 0.173399
## age_mo_c 1.209e-02 5.417e-03 8.980e+01 2.231 0.028157
## typicality_condition_c:age_mo_c 7.505e-03 8.220e-03 1.441e+03 0.913 0.361437
##
## (Intercept) ***
## typicality_condition_c
## age_mo_c *
## typicality_condition_c:age_mo_c
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) typc__ ag_m_c
## typclty_cn_ 0.050
## age_mo_c -0.060 0.000
## typclt__:__ 0.000 -0.086 0.058
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Quick Visualization
ggplot(trial_corrected_accuracy,aes(age,corrected_target_looking,color=condition))+
geom_point(alpha=0.1)+
geom_smooth()
ggplot(trial_critical_window_accuracy,aes(age,mean_accuracy,color=condition))+
geom_point(alpha=0.1)+
geom_smooth()
m <- lmer(mean_target_looking_critical ~ 1 + typicality_condition_c * age_mo_c + mean_target_looking_baseline +
(1 + typicality_condition_c|sub_num) +
(1|category),
data=trial_corrected_accuracy)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## mean_target_looking_critical ~ 1 + typicality_condition_c * age_mo_c +
## mean_target_looking_baseline + (1 + typicality_condition_c |
## sub_num) + (1 | category)
## Data: trial_corrected_accuracy
##
## REML criterion at convergence: 963.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.43074 -0.63866 0.04833 0.68425 2.00196
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## sub_num (Intercept) 0.002184 0.046735
## typicality_condition_c 0.000045 0.006708 1.00
## category (Intercept) 0.000996 0.031560
## Residual 0.076816 0.277156
## Number of obs: 3173, groups: sub_num, 86; category, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.111e-01 2.032e-02 6.806e+00 25.159 5.71e-08
## typicality_condition_c 1.686e-02 9.916e-03 1.881e+03 1.700 0.0893
## age_mo_c 1.158e-02 4.656e-03 8.858e+01 2.488 0.0147
## mean_target_looking_baseline 9.354e-02 2.172e-02 3.124e+03 4.308 1.70e-05
## typicality_condition_c:age_mo_c 5.763e-03 6.579e-03 1.943e+03 0.876 0.3811
##
## (Intercept) ***
## typicality_condition_c .
## age_mo_c *
## mean_target_looking_baseline ***
## typicality_condition_c:age_mo_c
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) typc__ ag_m_c mn_t__
## typclty_cn_ 0.018
## age_mo_c -0.026 0.000
## mn_trgt_lk_ -0.524 0.000 0.002
## typclt__:__ -0.003 -0.086 0.047 0.006
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Accuracy across age
Critical Window Only
ggplot(filter(avg_critical_window_accuracy,mean_age<700),aes(mean_age,accuracy))+
geom_pointrange(aes(ymin=lower_ci,ymax=upper_ci),
position=position_jitter(width=0.1),
width=0,
size=1.5) +
geom_hline(yintercept=0.5,linetype="dashed")+
geom_smooth(method="lm")+
xlab("Age (in days)")+
ylab("Proportion Target Looking\nduring the Critical Window")+
ylim(0,1)
ggsave(here::here("figures","age_relationship_critical_window_accuracy.png"),width=7,height=6)Baseline-corrected proportion target looking
# ggplot(avg_corrected_target_looking,aes(age,average_corrected_target_looking))+
# geom_pointrange(aes(ymin=lower_ci,ymax=upper_ci),
# position=position_jitter(width=0.1),
# width=0,
# size=1.5) +
# geom_hline(yintercept=0,linetype="dashed")+
# geom_smooth(method="lm")+
# xlab("Age (in months)")+
# ylab("Baseline-Corrected Proportion Target Looking")+
# ylim(-0.55,0.55)+
# scale_x_continuous(breaks=seq(12,18,1))
# ggsave(here::here("figures","age_relationship_baseline_corrected_accuracy.png"),width=7,height=6)Target Looking By Item
Next, we investigate item-level (target word) variation in proportion target looking.
Overall target looking during critical window and baseline window
First, we inspect overall target looking in the critical window and in the baseline window. Note the baseline effects, such that dog and cat are more likely to be fixated during baseline than bird and fish.
#average target looking during the baseline and critical window by item for each subject
avg_subj_target_looking_by_item <- d %>%
filter(exclude_participant==0) %>%
filter(useable_window==1) %>%
distinct(sub_num, age,age_mo, child_gender, trial_order,trial_number,category,target_image,target_typicality_z,mean_target_looking_critical,mean_target_looking_baseline,corrected_target_looking) %>%
group_by(sub_num, age,age_mo, child_gender, trial_order,category) %>%
summarize(N=n(),
mean_critical_accuracy=mean(mean_target_looking_critical,na.rm=TRUE),
ci=qt(0.975, N-1)*sd(mean_target_looking_critical,na.rm=T)/sqrt(N),
lower_ci=mean_critical_accuracy-ci,
upper_ci=mean_critical_accuracy+ci,
mean_baseline_accuracy=mean(mean_target_looking_baseline,na.rm=TRUE),
baseline_ci=qt(0.975, N-1)*sd(mean_target_looking_baseline,na.rm=T)/sqrt(N),
lower_baseline_ci=mean_baseline_accuracy-baseline_ci,
upper_baseline_ci=mean_baseline_accuracy+baseline_ci)
#summarize average target looking across subjects
avg_target_looking_by_item <- avg_subj_target_looking_by_item %>%
group_by(category) %>%
summarize(N=n(),
critical_accuracy=mean(mean_critical_accuracy,na.rm=TRUE),
ci=qt(0.975, N-1)*sd(mean_critical_accuracy,na.rm=T)/sqrt(N),
lower_ci=critical_accuracy-ci,
upper_ci=critical_accuracy+ci,
baseline_accuracy=mean(mean_baseline_accuracy,na.rm=TRUE),
baseline_ci=qt(0.975, N-1)*sd(mean_baseline_accuracy,na.rm=T)/sqrt(N),
lower_baseline_ci=baseline_accuracy-baseline_ci,
upper_baseline_ci=baseline_accuracy+baseline_ci)
avg_target_looking_by_item %>%
knitr::kable()| category | N | critical_accuracy | ci | lower_ci | upper_ci | baseline_accuracy | baseline_ci | lower_baseline_ci | upper_baseline_ci |
|---|---|---|---|---|---|---|---|---|---|
| bird | 167 | 0.5304431 | 0.0235691 | 0.5068741 | 0.5540122 | 0.4566338 | 0.0172812 | 0.4393527 | 0.4739150 |
| cat | 168 | 0.5966936 | 0.0224388 | 0.5742548 | 0.6191324 | 0.5181834 | 0.0178337 | 0.5003497 | 0.5360171 |
| dog | 167 | 0.5761305 | 0.0211010 | 0.5550295 | 0.5972315 | 0.5313491 | 0.0152826 | 0.5160665 | 0.5466317 |
| fish | 167 | 0.5193975 | 0.0245478 | 0.4948497 | 0.5439453 | 0.4610437 | 0.0167226 | 0.4443211 | 0.4777663 |
Target looking during the baseline and critical window split by age
#summarize average corrected target looking across subject
avg_target_looking_by_item_by_age <- avg_subj_target_looking_by_item %>%
mutate(age_group=ifelse(age_mo>16,"older than 16 months","younger than 16 months")) %>%
group_by(age_group,category) %>%
summarize(N=n(),
critical_accuracy=mean(mean_critical_accuracy,na.rm=TRUE),
ci=qt(0.975, N-1)*sd(mean_critical_accuracy,na.rm=T)/sqrt(N),
lower_ci=critical_accuracy-ci,
upper_ci=critical_accuracy+ci,
baseline_accuracy=mean(mean_baseline_accuracy,na.rm=TRUE),
baseline_ci=qt(0.975, N-1)*sd(mean_baseline_accuracy,na.rm=T)/sqrt(N),
lower_baseline_ci=baseline_accuracy-baseline_ci,
upper_baseline_ci=baseline_accuracy+baseline_ci)
avg_target_looking_by_item_by_age %>%
knitr::kable()| age_group | category | N | critical_accuracy | ci | lower_ci | upper_ci | baseline_accuracy | baseline_ci | lower_baseline_ci | upper_baseline_ci |
|---|---|---|---|---|---|---|---|---|---|---|
| older than 16 months | bird | 70 | 0.5529627 | 0.0362112 | 0.5167515 | 0.5891739 | 0.4510531 | 0.0271639 | 0.4238892 | 0.4782170 |
| older than 16 months | cat | 70 | 0.5970851 | 0.0315153 | 0.5655699 | 0.6286004 | 0.4953601 | 0.0268679 | 0.4684921 | 0.5222280 |
| older than 16 months | dog | 70 | 0.5787090 | 0.0331379 | 0.5455711 | 0.6118469 | 0.5334788 | 0.0204303 | 0.5130486 | 0.5539091 |
| older than 16 months | fish | 70 | 0.5679949 | 0.0337184 | 0.5342765 | 0.6017133 | 0.4641878 | 0.0298801 | 0.4343077 | 0.4940680 |
| younger than 16 months | bird | 97 | 0.5141919 | 0.0311829 | 0.4830090 | 0.5453748 | 0.4606611 | 0.0227628 | 0.4378983 | 0.4834240 |
| younger than 16 months | cat | 98 | 0.5964139 | 0.0316591 | 0.5647548 | 0.6280731 | 0.5344858 | 0.0236577 | 0.5108281 | 0.5581435 |
| younger than 16 months | dog | 97 | 0.5742698 | 0.0278494 | 0.5464203 | 0.6021192 | 0.5298122 | 0.0220962 | 0.5077160 | 0.5519083 |
| younger than 16 months | fish | 97 | 0.4843272 | 0.0333021 | 0.4510251 | 0.5176294 | 0.4587747 | 0.0195579 | 0.4392169 | 0.4783326 |
Overall baseline-corrected proportion target looking
Next, we investigate item-level variation in word recognition as measured by baseline-corrected proportion target looking (to help account for the baseline difference noted above).
#average corrected target looking by item for each subject
avg_subj_corrected_target_looking_by_item <- d %>%
filter(exclude_participant==0) %>%
filter(useable_window==1) %>%
mutate(age_group=ifelse(age_mo>16,"older than 16 months","younger than 16 months")) %>%
distinct(sub_num, months,age_mo,age_group, child_gender, trial_order,trial_number,category,target_image,target_typicality_z,mean_target_looking_critical,mean_target_looking_baseline,corrected_target_looking) %>%
group_by(sub_num, age_mo,age_group, child_gender, trial_order,category) %>%
summarize(N=n(),
average_corrected_target_looking=mean(corrected_target_looking,na.rm=TRUE),
ci=qt(0.975, N-1)*sd(corrected_target_looking,na.rm=T)/sqrt(N),
lower_ci=average_corrected_target_looking-ci,
upper_ci=average_corrected_target_looking+ci)
#summarize average corrected target looking across subject
avg_corrected_target_looking_by_item <- avg_subj_corrected_target_looking_by_item %>%
group_by(category) %>%
summarize(N=n(),
corrected_target_looking=mean(average_corrected_target_looking,na.rm=TRUE),
ci=qt(0.975, N-1)*sd(average_corrected_target_looking,na.rm=T)/sqrt(N),
lower_ci=corrected_target_looking-ci,
upper_ci=corrected_target_looking+ci)
avg_corrected_target_looking_by_item %>%
knitr::kable()| category | N | corrected_target_looking | ci | lower_ci | upper_ci |
|---|---|---|---|---|---|
| bird | 167 | 0.0738093 | 0.0289623 | 0.0448470 | 0.1027716 |
| cat | 168 | 0.0785102 | 0.0280725 | 0.0504377 | 0.1065827 |
| dog | 167 | 0.0447814 | 0.0253580 | 0.0194234 | 0.0701394 |
| fish | 167 | 0.0583538 | 0.0276439 | 0.0307099 | 0.0859978 |
Baseline-corrected proportion target looking split by age
#summarize average corrected target looking across subject
avg_corrected_target_looking_by_item_by_age <- avg_subj_corrected_target_looking_by_item %>%
group_by(age_group,category) %>%
summarize(N=n(),
corrected_target_looking=mean(average_corrected_target_looking,na.rm=TRUE),
ci=qt(0.975, N-1)*sd(average_corrected_target_looking,na.rm=T)/sqrt(N),
lower_ci=corrected_target_looking-ci,
upper_ci=corrected_target_looking+ci)
avg_corrected_target_looking_by_item_by_age %>%
knitr::kable()| age_group | category | N | corrected_target_looking | ci | lower_ci | upper_ci |
|---|---|---|---|---|---|---|
| older than 16 months | bird | 70 | 0.1019096 | 0.0459486 | 0.0559610 | 0.1478582 |
| older than 16 months | cat | 70 | 0.1017251 | 0.0428269 | 0.0588982 | 0.1445519 |
| older than 16 months | dog | 70 | 0.0452301 | 0.0368266 | 0.0084036 | 0.0820567 |
| older than 16 months | fish | 70 | 0.1038071 | 0.0415133 | 0.0622938 | 0.1453204 |
| younger than 16 months | bird | 97 | 0.0535308 | 0.0374123 | 0.0161185 | 0.0909430 |
| younger than 16 months | cat | 98 | 0.0619281 | 0.0374352 | 0.0244930 | 0.0993633 |
| younger than 16 months | dog | 97 | 0.0444576 | 0.0351927 | 0.0092649 | 0.0796503 |
| younger than 16 months | fish | 97 | 0.0255525 | 0.0361833 | -0.0106308 | 0.0617358 |
Aim 3
To test whether individual differences in word recognition or typicality effects are predicted by differences in experiences with each exemplar.
3.1
#zscore parent report of typicality within participants
parent_typicality_z <- d %>%
group_by(sub_num) %>%
mutate(target_parent_typicality_z = ((target_parent_typicality_rating - mean(target_parent_typicality_rating))/sd(target_parent_typicality_rating)),
distractor_parent_typicality_z = ((distractor_parent_typicality_rating - mean(distractor_parent_typicality_rating))/sd(distractor_parent_typicality_rating)))
parent_typicality <- parent_typicality_z %>%
filter(exclude_participant==0) %>%
filter(useable_window==1) %>%
distinct(sub_num,months,age_mo,child_gender, trial_order,trial_number,category,target_image,target_typicality_z,mean_target_looking_critical,mean_target_looking_baseline,corrected_target_looking,target_parent_typicality_z,distractor_parent_typicality_z,target_image,target_parent_typicality_rating,distractor_parent_typicality_rating) %>%
group_by(sub_num,age_mo,target_image, category,target_parent_typicality_z,target_parent_typicality_rating) %>%
summarize(N=n(),
average_corrected_target_looking=mean(corrected_target_looking,na.rm=TRUE)) %>%
na.omit(target_parent_typicality_rating)
#subject details for aim 3 analysis (how many participants have survey data)
aim3_subject_info <- parent_typicality %>%
ungroup()%>%
summarize(
N = length(unique(sub_num)),
mean_age = mean(age_mo),
sd_age = sd(age_mo)
)
aim3_subject_info%>%
knitr::kable()| N | mean_age | sd_age |
|---|---|---|
| 73 | 15.75181 | 1.481363 |
#model
m_3_1 <- lmer(average_corrected_target_looking ~ target_parent_typicality_z+age_mo+ (target_parent_typicality_z|sub_num) + (1|category), parent_typicality)
summary(m_3_1)#singular fit## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: average_corrected_target_looking ~ target_parent_typicality_z +
## age_mo + (target_parent_typicality_z | sub_num) + (1 | category)
## Data: parent_typicality
##
## REML criterion at convergence: 1956.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.14393 -0.62834 -0.02457 0.67409 2.79414
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## sub_num (Intercept) 0.0025253 0.050252
## target_parent_typicality_z 0.0000571 0.007556 1.00
## category (Intercept) 0.0002544 0.015951
## Residual 0.1184426 0.344155
## Number of obs: 2683, groups: sub_num, 73; category, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.087157 0.094115 79.014561 -0.926 0.3572
## target_parent_typicality_z 0.007682 0.006774 782.750835 1.134 0.2572
## age_mo 0.010016 0.005941 77.053129 1.686 0.0959 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trg___
## trgt_prnt__ -0.008
## age_mo -0.992 0.017
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')confint(m_3_1,method="Wald")## 2.5 % 97.5 %
## .sig01 NA NA
## .sig02 NA NA
## .sig03 NA NA
## .sig04 NA NA
## .sigma NA NA
## (Intercept) -0.271618051 0.09730458
## target_parent_typicality_z -0.005595628 0.02095922
## age_mo -0.001628631 0.02166042Quick Visualization
#by category
ggplot(parent_typicality,aes(target_parent_typicality_z,average_corrected_target_looking))+
geom_point(alpha=0.1)+
geom_smooth(method = "lm")+
facet_wrap(~category)
#by subject
ggplot(parent_typicality,aes(target_parent_typicality_z,average_corrected_target_looking))+
geom_point(alpha=0.1)+
geom_smooth(method = "lm")+
facet_wrap(~sub_num)
hist(parent_typicality$target_parent_typicality_z)
Aim 4
We will conduct a series of analyses to determine whether our results hold across a variety of different analytic decisions
4.1. Window
4.2 excluding unknown words
We registered using CDI responses as a way to remove unknown words; however we did not administer the CDI. Thus, we are not including this analysis since it would require an arbitrary cutoff for word recognition as a proxy for understanding a word (i.e., what is the difference between 50% accuracy and 50.01% accuracy)
Calculate CIs per word & per child
Remove any words where CIs overlap with childs CI around chance
Model
Is there a typicality effect when we remove unknown words?
4.3 Reaction time as the dependent measure
We will fit models analogous to those in 1.1 and 1.2 using reaction time as our primary dependent measure rather than accuracy
Load RT (computed using compute_RT.R & rt_helper.R)
rt_path <- here::here("data_analysis","registered_report","data","processed_data","CATegories_exp2_RT_by_trial.csv")
d_rt <- read_csv(rt_path)
d_rt<- trial_corrected_accuracy %>%
left_join(d_rt)Visualization of RTs
hist(filter(d_rt, shift_type=="D-T")$shift_start_rt)
The data are right skewed, which is common for RTs. We will use log transformations in the subsequent models to account for the distribution of the data.
Models
4.1 Subject-level RT
avg_subj_RT <- d_rt %>%
filter(shift_type=="D-T")%>%
group_by(sub_num, child_gender,condition) %>%
summarize(N=n(),
average_RT=mean(rt,na.rm=TRUE),
ci=qt(0.975, N-1)*sd(rt,na.rm=T)/sqrt(N),
lower_ci=average_RT-ci,
upper_ci=average_RT+ci)
avg_subj_RT <- avg_subj_RT %>%
mutate(
typicality_condition_c = case_when(
condition == "atypical" ~ -0.5,
condition == "typical" ~ 0.5,
TRUE ~ NA_real_
),
typicality_condition_typ = case_when(
condition == "atypical" ~ -1,
condition == "typical" ~ 0,
TRUE ~ NA_real_
),
typicality_condition_atyp = case_when(
condition == "atypical" ~ 0,
condition == "typical" ~ 1,
TRUE ~ NA_real_
),
)
m_4_1 <- lmer(log(average_RT) ~ 1 + typicality_condition_c + (1|sub_num),data=avg_subj_RT)
summary(m_4_1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(average_RT) ~ 1 + typicality_condition_c + (1 | sub_num)
## Data: avg_subj_RT
##
## REML criterion at convergence: 101.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.44553 -0.58448 0.06917 0.55077 1.80974
##
## Random effects:
## Groups Name Variance Std.Dev.
## sub_num (Intercept) 0.04722 0.2173
## Residual 0.06435 0.2537
## Number of obs: 172, groups: sub_num, 86
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.63866 0.03038 85.00000 218.494 <2e-16 ***
## typicality_condition_c -0.05198 0.03869 85.00000 -1.344 0.183
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## typclty_cn_ 0.000confint(m_4_1,method="Wald")## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) 6.5791045 6.69820650
## typicality_condition_c -0.1277983 0.02384749There is no significant typicality effect.
4.1.1 RT for typical
m_4_1_1 <- lmer(log(average_RT) ~ 1 + typicality_condition_typ + (1|sub_num),data=avg_subj_RT)
summary(m_4_1_1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(average_RT) ~ 1 + typicality_condition_typ + (1 | sub_num)
## Data: avg_subj_RT
##
## REML criterion at convergence: 101.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.44553 -0.58448 0.06917 0.55077 1.80974
##
## Random effects:
## Groups Name Variance Std.Dev.
## sub_num (Intercept) 0.04722 0.2173
## Residual 0.06435 0.2537
## Number of obs: 172, groups: sub_num, 86
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.61267 0.03602 144.17826 183.592 <2e-16 ***
## typicality_condition_typ -0.05198 0.03869 85.00000 -1.344 0.183
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## typclty_cn_ 0.537confint(m_4_1_1,method="Wald")## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) 6.5420732 6.68326241
## typicality_condition_typ -0.1277983 0.02384749RT for atypical
m_4_1_2 <- lmer(log(average_RT) ~ 1 + typicality_condition_atyp + (1|sub_num),data=avg_subj_RT)
summary(m_4_1_2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(average_RT) ~ 1 + typicality_condition_atyp + (1 | sub_num)
## Data: avg_subj_RT
##
## REML criterion at convergence: 101.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.44553 -0.58448 0.06917 0.55077 1.80974
##
## Random effects:
## Groups Name Variance Std.Dev.
## sub_num (Intercept) 0.04722 0.2173
## Residual 0.06435 0.2537
## Number of obs: 172, groups: sub_num, 86
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.66464 0.03602 144.17826 185.035 <2e-16 ***
## typicality_condition_atyp -0.05198 0.03869 85.00000 -1.344 0.183
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## typclty_cn_ -0.537confint(m_4_1_2,method="Wald")## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) 6.5940486 6.73523780
## typicality_condition_atyp -0.1277983 0.023847494.2 Trial level RT
trial_rt <- d_rt %>%
filter(shift_type=="D-T")%>%
mutate(
typicality_condition_c = case_when(
condition == "atypical" ~ -0.5,
condition == "typical" ~ 0.5,
TRUE ~ NA_real_
),
typicality_condition_typ = case_when(
condition == "atypical" ~ -1,
condition == "typical" ~ 0,
TRUE ~ NA_real_
),
typicality_condition_atyp = case_when(
condition == "atypical" ~ 0,
condition == "typical" ~ 1,
TRUE ~ NA_real_
),
)
m_4_2 <- lmer(log(rt) ~ 1 + typicality_condition_c +
(1+typicality_condition_c|sub_num) +
(1|category),
data=trial_rt)
summary(m_4_2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(rt) ~ 1 + typicality_condition_c + (1 + typicality_condition_c |
## sub_num) + (1 | category)
## Data: trial_rt
##
## REML criterion at convergence: 3274.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8850 -0.6552 0.1045 0.6364 2.4486
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## sub_num (Intercept) 0.0259295 0.16103
## typicality_condition_c 0.0079097 0.08894 -1.00
## category (Intercept) 0.0009335 0.03055
## Residual 0.5603636 0.74857
## Number of obs: 1423, groups: sub_num, 86; category, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.41260 0.03072 6.44238 208.729 1.39e-13 ***
## typicality_condition_c -0.03398 0.04120 370.63537 -0.825 0.41
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## typclty_cn_ -0.128
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')confint(m_4_1_2,method="Wald")## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) 6.5940486 6.73523780
## typicality_condition_atyp -0.1277983 0.02384749